论文标题
蜂窝连接的无人机的联合学习：无线电图和路径计划
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning
论文作者
论文摘要
为了延长无人驾驶汽车（UAV）的寿命，无人机需要在最短的时间内完成任务。除此要求外，在许多应用程序中，无人机还需要在飞行过程中使用可靠的互联网连接。在本文中，我们最大程度地减少了无人机的旅行时间，以确保满足概率连接性约束。为了解决这个问题，我们需要环境中停电概率的全局模型。由于无人机具有不同的任务并飞越不同的领域，因此他们收集的数据带有有关网络连接性的本地信息。结果，无人机不能依靠自己的经验来建立全球模型。这个问题影响了无人机的路径规划。为了解决这个问题，我们采用了两步方法。在第一步中，通过使用联合学习（FL），无人机协作建立了环境中停机概率的全局模型。在第二步中，通过使用在第一步中获得的全局模型和快速探索的随机树（RRT），我们提出了一种算法来优化无人机的路径。仿真结果显示了无人机网络这两步方法的有效性。
To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the network's connectivity. As a result, the UAVs can not rely on their own experiences to build the global model. This issue affects the path planning of the UAVs. To address this concern, we utilize a two-step approach. In the first step, by using Federated Learning (FL), the UAVs collaboratively build a global model of the outage probability in the environment. In the second step, by using the global model obtained in the first step and rapidly-exploring random trees (RRTs), we propose an algorithm to optimize UAVs' paths. Simulation results show the effectiveness of this two-step approach for UAV networks.
